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DocumentationCore ConceptsAI Memory Database

AI Memory Database

Purpose-built database architecture optimized for AI agent memory sharing, featuring advanced indexing, semantic search, and real-time synchronization capabilities.

Core TechnologyAdvanced

Overview

The infaza database is a revolutionary storage system specifically designed for AI agent memory management. Unlike traditional databases that focus on structured data, our system is optimized for the unique requirements of AI memory: semantic relationships, temporal context, and multi-dimensional embeddings.

Key Features

Semantic Indexing

Vector-based indexing for semantic similarity search

Temporal Contexts

Time-based memory organization and retrieval

Multi-Agent Access

Concurrent access with conflict resolution

Real-time Sync

Instant memory updates across agent teams

Database Architecture

The infaza database employs a hybrid architecture combining traditional relational structures with advanced vector databases and graph-based relationship mapping.

Storage Layer

Distributed storage system with automatic sharding and replication for high availability.

  • • SSD-optimized storage
  • • Automatic compression
  • • ACID compliance
  • • Point-in-time recovery
Vector Layer

High-performance vector database for embedding storage and similarity search operations.

  • • HNSW indexing
  • • Multi-dimensional vectors
  • • Sub-second search
  • • Dynamic updates
Graph Layer

Graph database for modeling complex relationships between memories and agent interactions.

  • • Relationship mapping
  • • Traversal optimization
  • • Context propagation
  • • Cycle detection

Memory Data Structures

Each memory object in the database contains multiple data types and metadata to support rich AI interactions and semantic understanding.

Memory Object Schema
{
  "memory_id": "uuid-v4",
  "agent_id": "agent-identifier",
  "team_id": "team-identifier",
  "content": {
    "text": "Natural language content",
    "embedding": [0.1, 0.2, ...], // 1536-dim vector
    "structured_data": {...},
    "media_refs": ["file-id-1", "file-id-2"]
  },
  "metadata": {
    "created_at": "ISO-8601 timestamp",
    "updated_at": "ISO-8601 timestamp",
    "access_level": "private|team|public",
    "tags": ["tag1", "tag2"],
    "importance": 0.85, // 0-1 scale
    "retention_policy": "duration|permanent"
  },
  "relationships": [
    {
      "target_memory_id": "related-memory-id",
      "relationship_type": "causal|temporal|semantic",
      "strength": 0.75 // 0-1 scale
    }
  ],
  "encryption": {
    "algorithm": "homomorphic-encryption",
    "key_id": "encryption-key-id",
    "encrypted_fields": ["content", "metadata"]
  }
}

Advanced Indexing System

Our multi-layered indexing system ensures fast retrieval across different query types while maintaining data consistency and supporting real-time updates.

Semantic Indexing

Vector-based indexing using state-of-the-art embedding models for semantic similarity search.

Index TypeHNSW + IVF
Dimensions1536
Similarity MetricCosine
Update Speed< 10ms
Temporal Indexing

Time-series indexing for efficient temporal queries and memory timeline reconstruction.

Index TypeB+ Tree
ResolutionMicrosecond
Range QueriesO(log n)
CompressionDelta Encoding

Memory Retrieval

infaza supports multiple retrieval patterns optimized for different AI agent use cases, from simple keyword searches to complex contextual queries.

Semantic Search

Find memories based on semantic similarity rather than exact keyword matches. Ideal for contextual AI responses.

memory.search_semantic("How to optimize neural networks", limit=10, threshold=0.8)

Temporal Queries

Retrieve memories from specific time periods or trace the evolution of concepts over time.

memory.search_temporal(start="2024-01-01", end="2024-12-31", topic="machine learning")

Graph Traversal

Follow relationship chains to discover connected memories and build comprehensive context.

memory.traverse_graph(start_id, max_depth=3, relationship_types=["causal", "semantic"])

Performance Characteristics

Storage

99.9%
Uptime SLA
Petabyte-scale storage

Search

<50ms
P95 Latency
Million+ vectors

Throughput

100K
Ops/second
Concurrent access

Horizontal Scaling

The database automatically scales across multiple nodes to handle growing memory requirements and increasing agent populations.

Auto-Scaling Features

Intelligent Sharding

Automatic data distribution based on access patterns and semantic clustering.

  • • Semantic-aware partitioning
  • • Dynamic rebalancing
  • • Hot-spot detection

Replication Strategy

Multi-region replication with eventual consistency and conflict resolution.

  • • 3x replication minimum
  • • Cross-region backup
  • • Automatic failover